package distributed

import (
	"context"
	"fmt"
	"sync"
	"time"

	protov1 "xagent/proto/v1"

	"github.com/asynkron/protoactor-go/actor"
)

// LearningManager handles distributed learning processes
type LearningManager struct {
	system         *actor.ActorSystem
	clusterManager *ClusterManager
	models         map[string]*protov1.DistributedLearning
	mutex          sync.RWMutex
}

// NewLearningManager creates a new learning manager
func NewLearningManager(system *actor.ActorSystem, clusterManager *ClusterManager) *LearningManager {
	return &LearningManager{
		system:         system,
		clusterManager: clusterManager,
		models:         make(map[string]*protov1.DistributedLearning),
	}
}

// StartTraining starts a new distributed training process
func (lm *LearningManager) StartTraining(ctx context.Context, modelID string, dataSources []string) (*protov1.DistributedLearning, error) {
	learning := &protov1.DistributedLearning{
		Id:          modelID,
		ModelId:     fmt.Sprintf("model-%d", time.Now().UnixNano()),
		DataSources: dataSources,
		Metrics:     make(map[string]float32),
		Status:      protov1.LearningStatus_LEARNING_STATUS_INITIALIZING,
		Progress:    0,
		Timestamp:   time.Now().UnixNano(),
	}

	lm.mutex.Lock()
	lm.models[learning.Id] = learning
	lm.mutex.Unlock()

	// Broadcast learning request to all nodes
	if err := lm.clusterManager.BroadcastMessage(learning); err != nil {
		return nil, fmt.Errorf("failed to broadcast learning: %v", err)
	}

	return learning, nil
}

// UpdateModel updates the model with new data
func (lm *LearningManager) UpdateModel(modelID string, metrics map[string]float32, progress float32, status protov1.LearningStatus) error {
	lm.mutex.Lock()
	defer lm.mutex.Unlock()

	model, exists := lm.models[modelID]
	if !exists {
		return fmt.Errorf("model not found: %s", modelID)
	}

	model.Metrics = metrics
	model.Progress = progress
	model.Status = status
	model.Timestamp = time.Now().UnixNano()

	return nil
}

// GetModel returns the current state of a model
func (lm *LearningManager) GetModel(modelID string) *protov1.DistributedLearning {
	lm.mutex.RLock()
	defer lm.mutex.RUnlock()

	return lm.models[modelID]
}

// ListModels returns all models
func (lm *LearningManager) ListModels() []*protov1.DistributedLearning {
	lm.mutex.RLock()
	defer lm.mutex.RUnlock()

	models := make([]*protov1.DistributedLearning, 0, len(lm.models))
	for _, model := range lm.models {
		models = append(models, model)
	}
	return models
}

// CleanupModels removes completed or failed models
func (lm *LearningManager) CleanupModels() {
	lm.mutex.Lock()
	defer lm.mutex.Unlock()

	for id, model := range lm.models {
		if model.Status == protov1.LearningStatus_LEARNING_STATUS_COMPLETED ||
			model.Status == protov1.LearningStatus_LEARNING_STATUS_FAILED {
			delete(lm.models, id)
		}
	}
}

// LearningActor handles learning-related messages
type LearningActor struct {
	manager *LearningManager
	actor.Actor
	localModel *LocalModel
}

// LocalModel represents local model state
type LocalModel struct {
	ID      string
	Metrics map[string]float32
	mutex   sync.RWMutex
}

func (la *LearningActor) Receive(context actor.Context) {
	switch msg := context.Message().(type) {
	case *protov1.DistributedLearning:
		la.handleLearning(msg)
	}
}

func (la *LearningActor) handleLearning(learning *protov1.DistributedLearning) {
	switch learning.Status {
	case protov1.LearningStatus_LEARNING_STATUS_INITIALIZING:
		la.initializeTraining(learning)
	case protov1.LearningStatus_LEARNING_STATUS_LEARNING:
		la.continueTraining(learning)
	case protov1.LearningStatus_LEARNING_STATUS_COMPLETED:
		la.handleCompletedTraining(learning)
	}
}

func (la *LearningActor) initializeTraining(learning *protov1.DistributedLearning) {
	// Initialize local model
	la.localModel = &LocalModel{
		ID:      learning.Id,
		Metrics: make(map[string]float32),
	}

	// Start local training
	go la.trainModel(learning)
}

func (la *LearningActor) continueTraining(learning *protov1.DistributedLearning) {
	if la.localModel == nil || la.localModel.ID != learning.Id {
		// Initialize if not already training
		la.initializeTraining(learning)
		return
	}

	// Update local model metrics
	la.localModel.mutex.Lock()
	la.localModel.Metrics = learning.Metrics
	la.localModel.mutex.Unlock()
}

func (la *LearningActor) handleCompletedTraining(learning *protov1.DistributedLearning) {
	if la.localModel != nil && la.localModel.ID == learning.Id {
		// Update local model with final metrics
		la.localModel.mutex.Lock()
		la.localModel.Metrics = learning.Metrics
		la.localModel.mutex.Unlock()
	}
}

func (la *LearningActor) trainModel(learning *protov1.DistributedLearning) {
	progress := float32(0)
	for progress < 1.0 {
		// Perform local training iteration
		metrics := performTrainingIteration(la.localModel)
		progress += 0.1 // Increment progress by 10%

		// Update manager with progress
		if err := la.manager.UpdateModel(learning.Id, metrics, progress, protov1.LearningStatus_LEARNING_STATUS_LEARNING); err != nil {
			fmt.Printf("Failed to update model: %v\n", err)
		}

		// Check if training should continue
		if shouldStopTraining(metrics) {
			break
		}

		time.Sleep(time.Second) // Prevent too frequent updates
	}

	// Mark as completed
	if err := la.manager.UpdateModel(learning.Id, la.localModel.Metrics, 1.0, protov1.LearningStatus_LEARNING_STATUS_COMPLETED); err != nil {
		fmt.Printf("Failed to complete model: %v\n", err)
	}
}

func performTrainingIteration(model *LocalModel) map[string]float32 {
	// Implement actual training logic here
	// This is a placeholder implementation
	metrics := make(map[string]float32)
	metrics["accuracy"] = 0.8
	metrics["loss"] = 0.2
	return metrics
}

func shouldStopTraining(metrics map[string]float32) bool {
	if accuracy, ok := metrics["accuracy"]; ok {
		return accuracy >= 0.95 // 95% accuracy threshold
	}
	return false
}
